Likelihood Ratio Statistic & P-value

In summary, the student attempted to solve question 1 of an homework equation, but is having difficulty understanding the procedure.
  • #1
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Homework Statement


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Homework Equations


So far I have only worked on question 1, as I was not able to solve it.
The likelihood ratio test statistic is defined as follows:
λ = 2 Log(L(theta-hat)/L(theta-hat_0))
Where L is the likelihood function, the product of all the pdfs/pmfs, and theta-hat is the maximum likelikhood estimator, the value of theta that maximizes the likelihood function. Theta-hat_0 is the same, but it is restricted to be in accordance with the H0 hypothesis.

I sincerely apologize for the lack of Latex, I am still learning how to work with it, I hope it didn't make it too vague.

The Attempt at a Solution



Alright, so what I've done so far is define the likelihood function as the product of the two pmfs, and I took theta to consist of p1 and p2, although I'm not sure that is allowed? Should I instead use theta = p1-p2?
L(p1,p2) = [tex]\text{p1}^x \binom{m}{x} (1-\text{p1})^{m-x}[/tex] * [tex]\text{p2}^y \binom{n}{y} (1-\text{p2})^{n-y}[/tex]

Now, finding theta-hat, I just took derivatives with respect to p1 and p2, and set it to zero. It gave me p1 = x / m and p2 = y / n.
I then did the same for theta-hat_0, but I first set p1 = p2 which gave me theta-hat_0 = (x+y) / (m+n)

However, plugging this all into the expression for λ doesn't give me a very pretty expression, which tells me that maybe I'm doing something wrong.

I get λ = [tex]2 \log \left(\left(\frac{x}{m}\right)^x \left(1-\frac{x}{m}\right)^{m-x} \left(\frac{y}{n}\right)^y
\left(1-\frac{y}{n}\right)^{n-y}\right)-2 \log \left(\left(\frac{x+y}{m+n}\right)^{x+y}
\left(1-\frac{x+y}{m+n}\right)^{m+n-x-y}\right)[/tex]

Could anyone indicate where I went wrong, if this is wrong? Maybe I'm not seeing some of the trivial simplifications here. After I figure out how to do this, I'll post where I get stuck with b!

Thank you

Edit: If it clarifies, I could post what my lamda is without filling in the values, but its basically 2Log of the pmfs multiplied with p1 = x/m, p2 = y/n, divided by the pmfs multiplied with p1 = p2 = m+n/(x+y)
 
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  • #2
I suppose it might not be wrong after all. I mean, if I plug the values in, I get something like 2.08, or something of the sort. Not a bad value by any means. However, I don't understand how to compute the P-value of this, sadly. Could anyone help me with that?
 
  • #3
Ok, so I suppose the answer is just right. I get lamda = 2.04, and the chi square distribution with 1 degree of freedom for 5% gives a higher value, so Ho is not rejected. The P-value I get is also bigger than .1, so it's not rejected. Thanks!
 

Related to Likelihood Ratio Statistic & P-value

1. What is the purpose of the Likelihood Ratio Statistic?

The Likelihood Ratio Statistic is used in statistical hypothesis testing to determine the strength of evidence against the null hypothesis. It compares the likelihood of the data under the null hypothesis to the likelihood of the data under an alternative hypothesis and provides a single numerical value that can be used to make a decision about the validity of the null hypothesis.

2. How is the Likelihood Ratio Statistic calculated?

The Likelihood Ratio Statistic is calculated by taking the ratio of the maximum likelihood of the data under the null hypothesis to the maximum likelihood of the data under the alternative hypothesis. It is often expressed as a log-likelihood ratio to simplify the calculation and interpretation.

3. What is a P-value and how is it related to the Likelihood Ratio Statistic?

A P-value is the probability of obtaining results at least as extreme as the observed results, assuming the null hypothesis is true. In other words, it represents the likelihood of observing the data if the null hypothesis is true. The Likelihood Ratio Statistic is used to calculate the P-value, with a smaller value indicating stronger evidence against the null hypothesis.

4. What is a commonly used threshold for determining significance with the Likelihood Ratio Statistic?

The most commonly used threshold for determining significance with the Likelihood Ratio Statistic is a P-value of 0.05. This means that if the calculated P-value is less than 0.05, the results are considered statistically significant and the null hypothesis can be rejected.

5. What are the limitations of the Likelihood Ratio Statistic?

One limitation of the Likelihood Ratio Statistic is that it assumes a specific distribution for the data, which may not always be accurate. Additionally, it can only be used for comparing two nested models, where one model is a simplified version of the other. Furthermore, it does not provide information about the direction of the effect, only the strength of evidence against the null hypothesis.

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